MutationForecaster® (mutationforecaster.com) is Cytognomix’s patented web-portal for analysis of all types of mutations (coding and non-coding), including interpretation, comparison and management of genetic variant data. It’s a fully automated genome interpretation solution for research, translational and clinical labs.
MutationForecaster® combines our world-leading genome interpretation software on your exome, gene panel, or complete genome (Shannon transcription factor and splicing pipelines, ASSEDA, Veridical) with the Cytognomix User Variation Database and Variant Effect Predictor. With our integrated suite of software products, analyze coding, non-coding, and copy number variants, and compare new results with existing or your own database. Select predicted mutations by phenotype using articles with CytoVisualization Analytics. With Workflows, automatically perform end-to-end analysis with all of our software products.
Download an 1 page overview of MutationForecaster®: link .
You can now experience our integrated suite of genome interpretation products through a free trial of MutationForecaster®. Once you register, analyze datasets that we have analyzed in our peer-reviewed publications with any of our software tools.
Ionizing radiation produces characteristic chromosome changes. The altered chromosomes contain two central constrictions, termed centromeres, instead of one (known as dicentric chromosomes [DCs]). Chromosome biodosimetry is approved by the IAEA for occupational radiation exposure, radiation emergencies, or monitoring long term exposures. In emergency responses to a range of doses, labs need efficient methods that identify DCs.
Cytognomix has developed a novel approach to find DCs that is independent of chromosome length, shape and structure from different laboratories (paper: TBME). The Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software works on multiple platforms and uses images produced by any of the existing automated metaphase capture systems found in most cytogenetic laboratories. ADCI is now available for for trial or purchase (link). Or contact us for details (pricing).
ADCI* uses machine learning based algorithms with high sensitivity and specificity that distinguish monocentric and dicentric chromosomes (Try the Dicentric Chromosome Identifier web app). With novel image segmentation, ADCI has become a fully functional cytogenetic biodosimetry system. ADCI takes images from all types of commercial metaphase scanning systems, selects high quality cells for analysis, identifies dicentric chromosomes (removing false positives), builds biodosimetry calibration curves, and estimates exposures. ADCI fulfills the criteria established by the IAEA for accurate triage biodosimetry of a sample in less than an hour. The accuracy is comparable to an experienced cytogeneticist. Check out our online user manual: wiki.
We find and validate mutations that others cannot with advanced, patented genomic probe and bioinformatic technologies. Cytognomix continues our long track record of creating technologies for genomic medicine. We anticipate and implement the needs of the biomedical and clinical genomics communities.
Browse the products section of the menu found in the header bar for more information regarding any of our services.
- Don’t want to run your own analyses on MutationForecaster®? Let us do it for you with our Bespoke Analysis Service.
- Customized genomic microarrays
- Ultrahigh resolution FISH probes:
- Microarray-based comparative genomic hybridization (aCGH) can use SC technology to increase reproducibility and reduce cost per sample.
CytoGnomix has delivered the Automated Dicentric Chromosome Identifier and Dose Estimator system to the Healthy Environments and Consumer Safety Branch at Health Canada, Government of Canada and to the Biodosimetry laboratory at Canadian Nuclear Laboratories. Each lab received two licensed versions of the product on a state of the art MSI portable computer and two full days […]
Our article: “Expedited radiation biodosimetry by automated dicentric chromosome identification and dose estimation” Ben Shirley 1^ , Yanxin Li 1^ , Joan H. M. Knoll 1,2 , and Peter K. Rogan 1,3 1 CytoGnomix, Departments of 2 Pathology and Laboratory Medicine and 3 Biochemistry, Western University, London, ON Canada has been accepted for publication in […]
CytoGnomix will be exhibiting at the 22nd Nuclear Medical Defense Conference, next week (May 8th to 11th 2017) in Munich, Germany. We will be introducing our biodosimetry product, the Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) at the meeting. We will also be presenting a poster on novel, patent pending methods to automatically […]
US Patent 9,624,549 has been issued! “Stable gene targets in breast cancer and use thereof for optimizing therapy. ” Peter K Rogan and Joan Knoll. (link) This technology is the basis of biochemically inspired chemotherapy prediction by machine learning (http://chemotherapy.cytognomix.com).
Rogan PK, Mucaki EJ, Baranova K, Dorman S, Knoll JHM. Predicting responses to chemotherapies by biochemically-inspired machine learning. Innovative Approaches to Optimal Cancer Care in Canada, Canadian Partnership against Cancer, Toronto, Apr. 6-8, 2017. (Link to abstract)
Accurate Cytogenetic Biodosimetry Through Automation Of Dicentric Chromosome Curation And Metaphase Cell Selection Jin Liu, Yaking Li, Ruth Wilkins, Farrah Flegal, Joan H. M. Knoll, Peter K. Rogan. bioRxiv, doi: https://doi.org/10.1101/120410 Abstract: Software to automate digital pathology relies on image quality and the rates of false positive and negative objects in these images. Cytogenetic biodosimetry […]
CytoGnomix has finalized our contract with Public Works Government Services Canada under the Build in Canada Innovation Program. This agreement licenses the Automated Dicentric Chromosome Identifier (ADCI) to the Consumer and Clinical Radiation Protection Bureau at Health Canada and Canadian Nuclear Laboratories and provides on-site training to these labs. These biodosimetry reference labs will test […]
We have published a new version of: Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning. F1000Research 2017, 5:2124 (doi:10.12688/f1000research.9417.2) The revision addresses the comments of the reviewers and adds several new analyses and results. Among our findings was the discovery of significant batch effects that, respectively, differentiate gene expression […]